Merge branch 'main' into issue-37210-to-sql-truncate

Author: gmcrocetti

ci/code_checks.sh

@@ -73,7 +73,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.NA SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
-        -i "pandas.Period.to_timestamp SA01" \
         -i "pandas.PeriodDtype.freq SA01" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
         -i "pandas.RangeIndex.start SA01" \
@@ -92,7 +91,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.day_name PR01,PR02" \
         -i "pandas.Series.dt.floor PR01,PR02" \
         -i "pandas.Series.dt.freq GL08" \
-        -i "pandas.Series.dt.microseconds SA01" \
         -i "pandas.Series.dt.month_name PR01,PR02" \
         -i "pandas.Series.dt.nanoseconds SA01" \
         -i "pandas.Series.dt.normalize PR01" \
@@ -109,20 +107,12 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Series.sparse.npoints SA01" \
         -i "pandas.Series.sparse.sp_values SA01" \
-        -i "pandas.Timedelta.asm8 SA01" \
-        -i "pandas.Timedelta.ceil SA01" \
         -i "pandas.Timedelta.components SA01" \
-        -i "pandas.Timedelta.floor SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
-        -i "pandas.Timedelta.round SA01" \
-        -i "pandas.Timedelta.to_numpy PR01" \
         -i "pandas.Timedelta.to_timedelta64 SA01" \
         -i "pandas.Timedelta.total_seconds SA01" \
-        -i "pandas.Timedelta.view SA01" \
-        -i "pandas.TimedeltaIndex.components SA01" \
-        -i "pandas.TimedeltaIndex.microseconds SA01" \
         -i "pandas.TimedeltaIndex.nanoseconds SA01" \
         -i "pandas.TimedeltaIndex.seconds SA01" \
         -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
@@ -131,10 +121,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.nanosecond GL08" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
-        -i "pandas.Timestamp.value GL08" \
         -i "pandas.Timestamp.year GL08" \
         -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.interchange.from_dataframe RT03,SA01" \
         -i "pandas.api.types.is_bool PR01,SA01" \
         -i "pandas.api.types.is_categorical_dtype SA01" \
         -i "pandas.api.types.is_complex PR01,SA01" \

doc/source/whatsnew/v2.3.0.rst

@@ -103,7 +103,8 @@ Conversion
 Strings
 ^^^^^^^
 - Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`59628`)
--
+- Bug in the ``center`` method on :class:`Series` and :class:`Index` object ``str`` accessors with pyarrow-backed dtype not matching the python behavior in corner cases with an odd number of fill characters (:issue:`54792`)
+
 
 Interval
 ^^^^^^^^

doc/source/whatsnew/v3.0.0.rst

@@ -53,6 +53,7 @@ Other enhancements
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
@@ -504,6 +505,7 @@ Performance improvements
 - :meth:`Series.str.extract` returns a :class:`RangeIndex` columns instead of an :class:`Index` column when possible (:issue:`57542`)
 - :meth:`Series.str.partition` with :class:`ArrowDtype` returns a :class:`RangeIndex` columns instead of an :class:`Index` column when possible (:issue:`57768`)
 - Performance improvement in :class:`DataFrame` when ``data`` is a ``dict`` and ``columns`` is specified (:issue:`24368`)
+- Performance improvement in :class:`MultiIndex` when setting :attr:`MultiIndex.names` doesn't invalidate all cached operations (:issue:`59578`)
 - Performance improvement in :meth:`DataFrame.join` for sorted but non-unique indexes (:issue:`56941`)
 - Performance improvement in :meth:`DataFrame.join` when left and/or right are non-unique and ``how`` is ``"left"``, ``"right"``, or ``"inner"`` (:issue:`56817`)
 - Performance improvement in :meth:`DataFrame.join` with ``how="left"`` or ``how="right"`` and ``sort=True`` (:issue:`56919`)
@@ -527,6 +529,7 @@ Performance improvements
 - Performance improvement in :meth:`RangeIndex.reindex` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57647`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.take` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57445`, :issue:`57752`)
 - Performance improvement in :func:`merge` if hash-join can be used (:issue:`57970`)
+- Performance improvement in :meth:`CategoricalDtype.update_dtype` when ``dtype`` is a :class:`CategoricalDtype` with non ``None`` categories and ordered (:issue:`59647`)
 - Performance improvement in :meth:`to_hdf` avoid unnecessary reopenings of the HDF5 file to speedup data addition to files with a very large number of groups . (:issue:`58248`)
 - Performance improvement in ``DataFrameGroupBy.__len__`` and ``SeriesGroupBy.__len__`` (:issue:`57595`)
 - Performance improvement in indexing operations for string dtypes (:issue:`56997`)

pandas/_libs/tslibs/period.pyx

@@ -2001,6 +2001,12 @@ cdef class _Period(PeriodMixin):
         -------
         Timestamp
 
+        See Also
+        --------
+        Timestamp : A class representing a single point in time.
+        Period : Represents a span of time with a fixed frequency.
+        PeriodIndex.to_timestamp : Convert a `PeriodIndex` to a `DatetimeIndex`.
+
         Examples
         --------
         >>> period = pd.Period('2023-1-1', freq='D')

pandas/_libs/tslibs/timedeltas.pyx

@@ -1421,9 +1421,16 @@ cdef class _Timedelta(timedelta):
         """
         Convert the Timedelta to a NumPy timedelta64.
 
-        This is an alias method for `Timedelta.to_timedelta64()`. The dtype and
-        copy parameters are available here only for compatibility. Their values
-        will not affect the return value.
+        This is an alias method for `Timedelta.to_timedelta64()`.
+
+        Parameters
+        ----------
+        dtype : NoneType
+            It is available here only for compatibility. Its value will not
+            affect the return value.
+        copy : bool, default False
+            It is available here only for compatibility. Its value will not
+            affect the return value.
 
         Returns
         -------
@@ -1451,11 +1458,26 @@ cdef class _Timedelta(timedelta):
         """
         Array view compatibility.
 
+        This method allows you to reinterpret the underlying data of a Timedelta
+        object as a different dtype. The `view` method provides a way to reinterpret
+        the internal representation of the `Timedelta` object without modifying its
+        data. This is particularly useful when you need to work with the underlying
+        data directly, such as for performance optimizations or interfacing with
+        low-level APIs. The returned value is typically the number of nanoseconds
+        since the epoch, represented as an integer or another specified dtype.
+
         Parameters
         ----------
         dtype : str or dtype
             The dtype to view the underlying data as.
 
+        See Also
+        --------
+        numpy.ndarray.view : Returns a view of an array with the same data.
+        Timedelta.to_numpy : Converts the Timedelta to a NumPy timedelta64.
+        Timedelta.total_seconds : Returns the total duration of the Timedelta
+            object in seconds.
+
         Examples
         --------
         >>> td = pd.Timedelta('3D')
@@ -1498,6 +1520,12 @@ cdef class _Timedelta(timedelta):
         numpy timedelta64 array scalar view
             Array scalar view of the timedelta in nanoseconds.
 
+        See Also
+        --------
+            Timedelta.total_seconds : Return the total seconds in the duration.
+            Timedelta.components : Return a namedtuple of the Timedelta's components.
+            Timedelta.to_timedelta64 : Convert the Timedelta to a numpy.timedelta64.
+
         Examples
         --------
         >>> td = pd.Timedelta('1 days 2 min 3 us 42 ns')
@@ -2061,6 +2089,12 @@ class Timedelta(_Timedelta):
         ------
         ValueError if the freq cannot be converted
 
+        See Also
+        --------
+            Timedelta.floor : Floor the Timedelta to the specified resolution.
+            Timedelta.round : Round the Timedelta to the nearest specified resolution.
+            Timestamp.ceil : Similar method for Timestamp objects.
+
         Examples
         --------
         >>> td = pd.Timedelta('1001ms')
@@ -2081,6 +2115,16 @@ class Timedelta(_Timedelta):
             Frequency string indicating the flooring resolution.
             It uses the same units as class constructor :class:`~pandas.Timedelta`.
 
+        Returns
+        -------
+        Timedelta
+            A new Timedelta object floored to the specified resolution.
+
+        See Also
+        --------
+            Timestamp.ceil : Round the Timestamp up to the nearest specified resolution.
+            Timestamp.round : Round the Timestamp to the nearest specified resolution.
+
         Examples
         --------
         >>> td = pd.Timedelta('1001ms')
@@ -2101,6 +2145,16 @@ class Timedelta(_Timedelta):
             Frequency string indicating the ceiling resolution.
             It uses the same units as class constructor :class:`~pandas.Timedelta`.
 
+        Returns
+        -------
+        Timedelta
+            A new Timedelta object ceiled to the specified resolution.
+
+        See Also
+        --------
+            Timedelta.floor : Floor the Timedelta to the specified resolution.
+            Timedelta.round : Round the Timedelta to the nearest specified resolution.
+
         Examples
         --------
         >>> td = pd.Timedelta('1001ms')

pandas/_libs/tslibs/timestamps.pyx

@@ -240,6 +240,27 @@ cdef class _Timestamp(ABCTimestamp):
 
     @property
     def value(self) -> int:
+        """
+        Return the value of the Timestamp.
+
+        Returns
+        -------
+        int
+            The integer representation of the Timestamp object in nanoseconds
+            since the Unix epoch (1970-01-01 00:00:00 UTC).
+
+        See Also
+        --------
+        Timestamp.second : Return the second of the Timestamp.
+        Timestamp.minute : Return the minute of the Timestamp.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp("2024-08-31 16:16:30")
+        >>> ts.value
+        1725120990000000000
+        """
+
         try:
             return convert_reso(self._value, self._creso, NPY_FR_ns, False)
         except OverflowError:
@@ -1020,8 +1041,8 @@ cdef class _Timestamp(ABCTimestamp):
 
         See Also
         --------
-        Timestamp.day : Return the day of the year.
-        Timestamp.year : Return the year of the week.
+        Timestamp.day : Return the day of the Timestamp.
+        Timestamp.year : Return the year of the Timestamp.
 
         Examples
         --------

pandas/_testing/asserters.py

@@ -188,7 +188,7 @@ def assert_index_equal(
     check_order: bool = True,
     rtol: float = 1.0e-5,
     atol: float = 1.0e-8,
-    obj: str = "Index",
+    obj: str | None = None,
 ) -> None:
     """
     Check that left and right Index are equal.
@@ -217,7 +217,7 @@ def assert_index_equal(
         Relative tolerance. Only used when check_exact is False.
     atol : float, default 1e-8
         Absolute tolerance. Only used when check_exact is False.
-    obj : str, default 'Index'
+    obj : str, default 'Index' or 'MultiIndex'
         Specify object name being compared, internally used to show appropriate
         assertion message.
 
@@ -235,6 +235,9 @@ def assert_index_equal(
     """
     __tracebackhide__ = True
 
+    if obj is None:
+        obj = "MultiIndex" if isinstance(left, MultiIndex) else "Index"
+
     def _check_types(left, right, obj: str = "Index") -> None:
         if not exact:
             return
@@ -283,7 +286,7 @@ def _check_types(left, right, obj: str = "Index") -> None:
         right = cast(MultiIndex, right)
 
         for level in range(left.nlevels):
-            lobj = f"MultiIndex level [{level}]"
+            lobj = f"{obj} level [{level}]"
             try:
                 # try comparison on levels/codes to avoid densifying MultiIndex
                 assert_index_equal(
@@ -314,7 +317,7 @@ def _check_types(left, right, obj: str = "Index") -> None:
                     obj=lobj,
                 )
             # get_level_values may change dtype
-            _check_types(left.levels[level], right.levels[level], obj=obj)
+            _check_types(left.levels[level], right.levels[level], obj=lobj)
 
     # skip exact index checking when `check_categorical` is False
     elif check_exact and check_categorical:
@@ -527,7 +530,7 @@ def assert_interval_array_equal(
         kwargs["check_freq"] = False
 
     assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs)
-    assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs)
+    assert_equal(left._right, right._right, obj=f"{obj}.right", **kwargs)
 
     assert_attr_equal("closed", left, right, obj=obj)
 

pandas/core/array_algos/quantile.py

@@ -91,7 +91,7 @@ def quantile_with_mask(
     if is_empty:
         # create the array of na_values
         # 2d len(values) * len(qs)
-        flat = np.array([fill_value] * len(qs))
+        flat = np.full(len(qs), fill_value)
         result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
     else:
         result = _nanquantile(